{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "40560d13",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "# Transformer\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3473d007",
   "metadata": {},
   "source": [
    "Transformer架构"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d4f649",
   "metadata": {},
   "source": [
    "- 基于编码器-解码器架构来处理序列对\n",
    "- 跟使用注意力的seq2seq不同，Transformer是纯基于自注意力，没有RNN"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3262691c",
   "metadata": {},
   "source": [
    "<img src='transformer.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ebfa4d1",
   "metadata": {},
   "source": [
    "多头注意力\n",
    "- 对同一key，value，query，希望抽取不同的信息\n",
    "    - 例如短距离关系和长距离关系\n",
    "    - 类似卷积的多输出通道\n",
    "<img src='multi-head1.png' style=\"float:center\">\n",
    "- 多头注意力使用h个独立的注意力池化\n",
    "    - 合并各个头(head)输出得到最终输出\n",
    "    - 下图显示两个头\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b315f7",
   "metadata": {},
   "source": [
    "<img src='multi-head.png'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c2990d9",
   "metadata": {},
   "source": [
    "有掩码的多头注意力\n",
    "    <img src='multi-head2.png' align='top'>\n",
    "- 解码器对序列中一个元素输出时，不应该考虑该元素之后的元素\n",
    "- 可以通过掩码来实现\n",
    "    - 也就是计算$x_i$，输出时，假装当前序列长度为$i$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c46a8a80",
   "metadata": {},
   "source": [
    "基于位置的前馈网络\n",
    "- 将输入形状由(b, n,d)变换成(bn, d)\n",
    "<img src='multi-head3.png' align='top' style='float'>\n",
    "- 作用两个全连接层\n",
    "- 输出形状由(bn, d)变化回(b, n, d)\n",
    "- 等价于两层核窗口为1的一维卷积层\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "098f8f8e",
   "metadata": {},
   "source": [
    "层归一化\n",
    "- 批量归一化对每个特征/通道里元素进行归一化\n",
    "<img src='transformer_add.png' align='middle'>\n",
    "    - 不适合序列长度会变的NLP应用\n",
    "- 层归一化对每个样本里的元素进行归一化\n",
    "\n",
    "<img src='add_norm.png' width='800'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dba5fd75",
   "metadata": {},
   "source": [
    "信息传递\n",
    "<img src='transformer_transfer.png' align='top'>\n",
    "- 编码器最后的输出$y_1,\\cdots,y_n$\n",
    "- 将其作为解码器中第i个Transformer块中多头注意力的key和value\n",
    "    - 它的query来自目标序列\n",
    "    - 每个解码器中的Transformer块中的注意力层（类似seq2seq中的attention，不是自注意力，是正常的attention层，key和value来自于encoder）\n",
    "- 编码器和解码器中块的个数和输出维度一般一样的\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed347e18",
   "metadata": {},
   "source": [
    "预测\n",
    "- 预测第$t+1$个输出时\n",
    "- 解码器中输入前$t$个预测值\n",
    "    - 在自注意力中，前$t$个预测值作为key和value，第$t$个预测值还作为query\n",
    "\n",
    "<img src='transformer_forecast.png' width='800'>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "855f5068",
   "metadata": {},
   "source": [
    "总结\n",
    "- Transformer是一个纯使用注意力的编码-解码器\n",
    "- 编码器和解码器都有n个transformer块\n",
    "- 每个块里使用多头(自)注意力，基于位置的前馈网络，和层归一化\n",
    "\n",
    "<img src='transformer1.png' width='400'>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b42900a1",
   "metadata": {},
   "source": [
    "# 代码"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb05d3d7",
   "metadata": {},
   "source": [
    "## 多头注意力\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c490388f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32a7956b",
   "metadata": {},
   "source": [
    "选择缩放点积注意力作为每一个注意力头"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5aada1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "class MultiHeadAttention(nn.Module):\n",
    "    \"\"\"多头注意力\"\"\"\n",
    "    def __init__(self, key_size, query_size, value_size, num_hiddens,\n",
    "                 num_heads, dropout, bias=False, **kwargs):\n",
    "        super(MultiHeadAttention, self).__init__(**kwargs)\n",
    "        self.num_heads = num_heads\n",
    "        self.attention = d2l.DotProductAttention(dropout)\n",
    "        self.W_q = nn.Linear(query_size, num_hiddens, bias=bias)\n",
    "        self.W_k = nn.Linear(key_size, num_hiddens, bias=bias)\n",
    "        self.W_v = nn.Linear(value_size, num_hiddens, bias=bias)\n",
    "        self.W_o = nn.Linear(num_hiddens, num_hiddens, bias=bias)\n",
    "\n",
    "    def forward(self, queries, keys, values, valid_lens):\n",
    "        queries = transpose_qkv(self.W_q(queries), self.num_heads)\n",
    "        keys = transpose_qkv(self.W_k(keys), self.num_heads)\n",
    "        values = transpose_qkv(self.W_v(values), self.num_heads)\n",
    "\n",
    "        if valid_lens is not None:\n",
    "            valid_lens = torch.repeat_interleave(\n",
    "                valid_lens, repeats=self.num_heads, dim=0)\n",
    "\n",
    "        output = self.attention(queries, keys, values, valid_lens)\n",
    "\n",
    "        output_concat = transpose_output(output, self.num_heads)\n",
    "        return self.W_o(output_concat)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31b06eba",
   "metadata": {},
   "source": [
    "使多个头并行计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d60298d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def transpose_qkv(X, num_heads):\n",
    "    \"\"\"为了多注意力头的并行计算而变换形状\"\"\"\n",
    "    X = X.reshape(X.shape[0], X.shape[1], num_heads, -1)\n",
    "\n",
    "    X = X.permute(0, 2, 1, 3)\n",
    "\n",
    "    return X.reshape(-1, X.shape[2], X.shape[3])\n",
    "\n",
    "\n",
    "def transpose_output(X, num_heads):\n",
    "    \"\"\"逆转transpose_qkv函数的操作\"\"\"\n",
    "    X = X.reshape(-1, num_heads, X.shape[1], X.shape[2])\n",
    "    X = X.permute(0, 2, 1, 3)\n",
    "    return X.reshape(X.shape[0], X.shape[1], -1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e6f4e73",
   "metadata": {},
   "source": [
    "测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dadbb4eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MultiHeadAttention(\n",
       "  (attention): DotProductAttention(\n",
       "    (dropout): Dropout(p=0.5, inplace=False)\n",
       "  )\n",
       "  (W_q): Linear(in_features=100, out_features=100, bias=False)\n",
       "  (W_k): Linear(in_features=100, out_features=100, bias=False)\n",
       "  (W_v): Linear(in_features=100, out_features=100, bias=False)\n",
       "  (W_o): Linear(in_features=100, out_features=100, bias=False)\n",
       ")"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_hiddens, num_heads = 100, 5\n",
    "attention = MultiHeadAttention(num_hiddens, num_hiddens, num_hiddens,\n",
    "                               num_hiddens, num_heads, 0.5)\n",
    "attention.eval()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b92d9e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 4, 100])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "batch_size, num_queries = 2, 4\n",
    "num_kvpairs, valid_lens =  6, torch.tensor([3, 2])\n",
    "X = torch.ones((batch_size, num_queries, num_hiddens))\n",
    "Y = torch.ones((batch_size, num_kvpairs, num_hiddens))\n",
    "attention(X, Y, Y, valid_lens).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31ff872d",
   "metadata": {},
   "source": [
    "## Transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "97904f88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:51.151536Z",
     "iopub.status.busy": "2022-07-31T03:01:51.150930Z",
     "iopub.status.idle": "2022-07-31T03:01:53.130502Z",
     "shell.execute_reply": "2022-07-31T03:01:53.129568Z"
    },
    "origin_pos": 2,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "import math\n",
    "import pandas as pd\n",
    "import torch\n",
    "from torch import nn\n",
    "from d2l import torch as d2l"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edde8195",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "基于位置的前馈网络"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "534dc7e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:53.135645Z",
     "iopub.status.busy": "2022-07-31T03:01:53.135235Z",
     "iopub.status.idle": "2022-07-31T03:01:53.141481Z",
     "shell.execute_reply": "2022-07-31T03:01:53.140610Z"
    },
    "origin_pos": 6,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class PositionWiseFFN(nn.Module):\n",
    "    \"\"\"基于位置的前馈网络\"\"\"\n",
    "    def __init__(self, ffn_num_input, ffn_num_hiddens, ffn_num_outputs,\n",
    "                 **kwargs):\n",
    "        super(PositionWiseFFN, self).__init__(**kwargs)\n",
    "        self.dense1 = nn.Linear(ffn_num_input, ffn_num_hiddens)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dense2 = nn.Linear(ffn_num_hiddens, ffn_num_outputs)\n",
    "\n",
    "    def forward(self, X):\n",
    "        return self.dense2(self.relu(self.dense1(X)))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "697e31a3",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "改变张量的最里层维度的尺寸"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "acd6c21f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:53.145862Z",
     "iopub.status.busy": "2022-07-31T03:01:53.145458Z",
     "iopub.status.idle": "2022-07-31T03:01:53.155277Z",
     "shell.execute_reply": "2022-07-31T03:01:53.154413Z"
    },
    "origin_pos": 10,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-1.2386, -0.9917,  0.4708, -0.4516, -0.3069,  0.0447,  0.9740,  0.1313],\n",
       "        [-1.2386, -0.9917,  0.4708, -0.4516, -0.3069,  0.0447,  0.9740,  0.1313],\n",
       "        [-1.2386, -0.9917,  0.4708, -0.4516, -0.3069,  0.0447,  0.9740,  0.1313]],\n",
       "       grad_fn=<SelectBackward0>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ffn = PositionWiseFFN(4, 4, 8)\n",
    "ffn.eval()\n",
    "ffn(torch.ones((2, 3, 4)))[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "731afae0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "对比不同维度的层规范化和批量规范化的效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "226dff63",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:53.158307Z",
     "iopub.status.busy": "2022-07-31T03:01:53.158031Z",
     "iopub.status.idle": "2022-07-31T03:01:53.167695Z",
     "shell.execute_reply": "2022-07-31T03:01:53.166957Z"
    },
    "origin_pos": 14,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "layer norm: tensor([[-1.0000,  1.0000],\n",
      "        [-1.0000,  1.0000]], grad_fn=<NativeLayerNormBackward0>) \n",
      "batch norm: tensor([[-1.0000, -1.0000],\n",
      "        [ 1.0000,  1.0000]], grad_fn=<NativeBatchNormBackward0>)\n"
     ]
    }
   ],
   "source": [
    "ln = nn.LayerNorm(2)\n",
    "bn = nn.BatchNorm1d(2)\n",
    "X = torch.tensor([[1, 2], [2, 3]], dtype=torch.float32)\n",
    "print('layer norm:', ln(X), '\\nbatch norm:', bn(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6eb165bc",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "使用残差连接和层规范化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "0b79dc94",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:53.170695Z",
     "iopub.status.busy": "2022-07-31T03:01:53.170340Z",
     "iopub.status.idle": "2022-07-31T03:01:53.175232Z",
     "shell.execute_reply": "2022-07-31T03:01:53.174526Z"
    },
    "origin_pos": 18,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class AddNorm(nn.Module):\n",
    "    \"\"\"残差连接后进行层规范化\"\"\"\n",
    "    def __init__(self, normalized_shape, dropout, **kwargs):\n",
    "        super(AddNorm, self).__init__(**kwargs)\n",
    "        self.dropout = nn.Dropout(dropout)\n",
    "        self.ln = nn.LayerNorm(normalized_shape)\n",
    "\n",
    "    def forward(self, X, Y):\n",
    "        return self.ln(self.dropout(Y) + X)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f054a39",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "加法操作后输出张量的形状相同"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e9a5e8b1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:53.178489Z",
     "iopub.status.busy": "2022-07-31T03:01:53.177918Z",
     "iopub.status.idle": "2022-07-31T03:01:53.185116Z",
     "shell.execute_reply": "2022-07-31T03:01:53.184484Z"
    },
    "origin_pos": 22,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 3, 4])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_norm = AddNorm([3, 4], 0.5)\n",
    "add_norm.eval()\n",
    "add_norm(torch.ones((2, 3, 4)), torch.ones((2, 3, 4))).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da0408e",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "实现编码器中的一个层"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0619ca28",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:53.189671Z",
     "iopub.status.busy": "2022-07-31T03:01:53.189151Z",
     "iopub.status.idle": "2022-07-31T03:01:53.195245Z",
     "shell.execute_reply": "2022-07-31T03:01:53.194560Z"
    },
    "origin_pos": 26,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class EncoderBlock(nn.Module):\n",
    "    \"\"\"transformer编码器块\"\"\"\n",
    "    def __init__(self, key_size, query_size, value_size, num_hiddens,\n",
    "                 norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,\n",
    "                 dropout, use_bias=False, **kwargs):\n",
    "        super(EncoderBlock, self).__init__(**kwargs)\n",
    "        self.attention = d2l.MultiHeadAttention(\n",
    "            key_size, query_size, value_size, num_hiddens, num_heads, dropout,\n",
    "            use_bias)\n",
    "        self.addnorm1 = AddNorm(norm_shape, dropout)\n",
    "        self.ffn = PositionWiseFFN(\n",
    "            ffn_num_input, ffn_num_hiddens, num_hiddens)\n",
    "        self.addnorm2 = AddNorm(norm_shape, dropout)\n",
    "\n",
    "    def forward(self, X, valid_lens):\n",
    "        Y = self.addnorm1(X, self.attention(X, X, X, valid_lens))\n",
    "        return self.addnorm2(Y, self.ffn(Y))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fda0e222",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Transformer编码器中的任何层都不会改变其输入的形状"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a72f89f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:53.199528Z",
     "iopub.status.busy": "2022-07-31T03:01:53.199037Z",
     "iopub.status.idle": "2022-07-31T03:01:53.210586Z",
     "shell.execute_reply": "2022-07-31T03:01:53.209930Z"
    },
    "origin_pos": 30,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 100, 24])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = torch.ones((2, 100, 24))\n",
    "valid_lens = torch.tensor([3, 2])\n",
    "encoder_blk = EncoderBlock(24, 24, 24, 24, [100, 24], 24, 48, 8, 0.5)\n",
    "encoder_blk.eval()\n",
    "encoder_blk(X, valid_lens).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a44a878",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Transformer编码器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "be0d244b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:53.214931Z",
     "iopub.status.busy": "2022-07-31T03:01:53.214431Z",
     "iopub.status.idle": "2022-07-31T03:01:53.222175Z",
     "shell.execute_reply": "2022-07-31T03:01:53.221497Z"
    },
    "origin_pos": 34,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class TransformerEncoder(d2l.Encoder):\n",
    "    \"\"\"transformer编码器\"\"\"\n",
    "    def __init__(self, vocab_size, key_size, query_size, value_size,\n",
    "                 num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                 num_heads, num_layers, dropout, use_bias=False, **kwargs):\n",
    "        super(TransformerEncoder, self).__init__(**kwargs)\n",
    "        self.num_hiddens = num_hiddens\n",
    "        self.embedding = nn.Embedding(vocab_size, num_hiddens)\n",
    "        self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)\n",
    "        self.blks = nn.Sequential()\n",
    "        for i in range(num_layers):\n",
    "            self.blks.add_module(\"block\"+str(i),\n",
    "                EncoderBlock(key_size, query_size, value_size, num_hiddens,\n",
    "                             norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                             num_heads, dropout, use_bias))\n",
    "\n",
    "    def forward(self, X, valid_lens, *args):\n",
    "        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))\n",
    "        self.attention_weights = [None] * len(self.blks)\n",
    "        for i, blk in enumerate(self.blks):\n",
    "            X = blk(X, valid_lens)\n",
    "            self.attention_weights[\n",
    "                i] = blk.attention.attention.attention_weights\n",
    "        return X"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e3c9998",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "创建一个两层的transformer编码器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6867b160",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:53.226775Z",
     "iopub.status.busy": "2022-07-31T03:01:53.226296Z",
     "iopub.status.idle": "2022-07-31T03:01:53.240164Z",
     "shell.execute_reply": "2022-07-31T03:01:53.239521Z"
    },
    "origin_pos": 38,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 100, 24])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoder = TransformerEncoder(\n",
    "    200, 24, 24, 24, 24, [100, 24], 24, 48, 8, 2, 0.5)\n",
    "encoder.eval()\n",
    "encoder(torch.ones((2, 100), dtype=torch.long), valid_lens).shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52d082e0",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Transformer解码器也是由多个相同的层组成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6ef5a28f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:53.244576Z",
     "iopub.status.busy": "2022-07-31T03:01:53.244075Z",
     "iopub.status.idle": "2022-07-31T03:01:53.253809Z",
     "shell.execute_reply": "2022-07-31T03:01:53.253090Z"
    },
    "origin_pos": 42,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [],
   "source": [
    "class DecoderBlock(nn.Module):\n",
    "    \"\"\"解码器中第i个块\"\"\"\n",
    "    def __init__(self, key_size, query_size, value_size, num_hiddens,\n",
    "                 norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,\n",
    "                 dropout, i, **kwargs):\n",
    "        super(DecoderBlock, self).__init__(**kwargs)\n",
    "        self.i = i\n",
    "        self.attention1 = d2l.MultiHeadAttention(\n",
    "            key_size, query_size, value_size, num_hiddens, num_heads, dropout)\n",
    "        self.addnorm1 = AddNorm(norm_shape, dropout)\n",
    "        self.attention2 = d2l.MultiHeadAttention(\n",
    "            key_size, query_size, value_size, num_hiddens, num_heads, dropout)\n",
    "        self.addnorm2 = AddNorm(norm_shape, dropout)\n",
    "        self.ffn = PositionWiseFFN(ffn_num_input, ffn_num_hiddens,\n",
    "                                   num_hiddens)\n",
    "        self.addnorm3 = AddNorm(norm_shape, dropout)\n",
    "\n",
    "    def forward(self, X, state):\n",
    "        enc_outputs, enc_valid_lens = state[0], state[1]\n",
    "        if state[2][self.i] is None:\n",
    "            key_values = X\n",
    "        else:\n",
    "            key_values = torch.cat((state[2][self.i], X), axis=1)\n",
    "        state[2][self.i] = key_values\n",
    "        if self.training:\n",
    "            batch_size, num_steps, _ = X.shape\n",
    "            dec_valid_lens = torch.arange(\n",
    "                1, num_steps + 1, device=X.device).repeat(batch_size, 1)\n",
    "        else:\n",
    "            dec_valid_lens = None\n",
    "\n",
    "        X2 = self.attention1(X, key_values, key_values, dec_valid_lens)\n",
    "        Y = self.addnorm1(X, X2)\n",
    "        Y2 = self.attention2(Y, enc_outputs, enc_outputs, enc_valid_lens)\n",
    "        Z = self.addnorm2(Y, Y2)\n",
    "        return self.addnorm3(Z, self.ffn(Z)), state"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52fe9560",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "编码器和解码器的特征维度都是`num_hiddens`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0a44fc48",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:53.258104Z",
     "iopub.status.busy": "2022-07-31T03:01:53.257608Z",
     "iopub.status.idle": "2022-07-31T03:01:53.272238Z",
     "shell.execute_reply": "2022-07-31T03:01:53.271545Z"
    },
    "origin_pos": 46,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 100, 24])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "decoder_blk = DecoderBlock(24, 24, 24, 24, [100, 24], 24, 48, 8, 0.5, 0)\n",
    "decoder_blk.eval()\n",
    "X = torch.ones((2, 100, 24))\n",
    "state = [encoder_blk(X, valid_lens), valid_lens, [None]]\n",
    "decoder_blk(X, state)[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1600935",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Transformer解码器"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d19ed317",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:53.276720Z",
     "iopub.status.busy": "2022-07-31T03:01:53.276225Z",
     "iopub.status.idle": "2022-07-31T03:01:53.285192Z",
     "shell.execute_reply": "2022-07-31T03:01:53.284530Z"
    },
    "origin_pos": 50,
    "tab": [
     "pytorch"
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   "outputs": [],
   "source": [
    "class TransformerDecoder(d2l.AttentionDecoder):\n",
    "    def __init__(self, vocab_size, key_size, query_size, value_size,\n",
    "                 num_hiddens, norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                 num_heads, num_layers, dropout, **kwargs):\n",
    "        super(TransformerDecoder, self).__init__(**kwargs)\n",
    "        self.num_hiddens = num_hiddens\n",
    "        self.num_layers = num_layers\n",
    "        self.embedding = nn.Embedding(vocab_size, num_hiddens)\n",
    "        self.pos_encoding = d2l.PositionalEncoding(num_hiddens, dropout)\n",
    "        self.blks = nn.Sequential()\n",
    "        for i in range(num_layers):\n",
    "            self.blks.add_module(\"block\"+str(i),\n",
    "                DecoderBlock(key_size, query_size, value_size, num_hiddens,\n",
    "                             norm_shape, ffn_num_input, ffn_num_hiddens,\n",
    "                             num_heads, dropout, i))\n",
    "        self.dense = nn.Linear(num_hiddens, vocab_size)\n",
    "\n",
    "    def init_state(self, enc_outputs, enc_valid_lens, *args):\n",
    "        return [enc_outputs, enc_valid_lens, [None] * self.num_layers]\n",
    "\n",
    "    def forward(self, X, state):\n",
    "        X = self.pos_encoding(self.embedding(X) * math.sqrt(self.num_hiddens))\n",
    "        self._attention_weights = [[None] * len(self.blks) for _ in range (2)]\n",
    "        for i, blk in enumerate(self.blks):\n",
    "            X, state = blk(X, state)\n",
    "            self._attention_weights[0][\n",
    "                i] = blk.attention1.attention.attention_weights\n",
    "            self._attention_weights[1][\n",
    "                i] = blk.attention2.attention.attention_weights\n",
    "        return self.dense(X), state\n",
    "\n",
    "    @property\n",
    "    def attention_weights(self):\n",
    "        return self._attention_weights"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93e4e320",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c359c479",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:01:53.288973Z",
     "iopub.status.busy": "2022-07-31T03:01:53.288504Z",
     "iopub.status.idle": "2022-07-31T03:03:11.619636Z",
     "shell.execute_reply": "2022-07-31T03:03:11.618932Z"
    },
    "origin_pos": 54,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loss 0.033, 5993.4 tokens/sec on cuda:0\n"
     ]
    },
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   "source": [
    "num_hiddens, num_layers, dropout, batch_size, num_steps = 32, 2, 0.1, 64, 10\n",
    "lr, num_epochs, device = 0.005, 200, d2l.try_gpu()\n",
    "ffn_num_input, ffn_num_hiddens, num_heads = 32, 64, 4\n",
    "key_size, query_size, value_size = 32, 32, 32\n",
    "norm_shape = [32]\n",
    "\n",
    "train_iter, src_vocab, tgt_vocab = d2l.load_data_nmt(batch_size, num_steps)\n",
    "\n",
    "encoder = TransformerEncoder(\n",
    "    len(src_vocab), key_size, query_size, value_size, num_hiddens,\n",
    "    norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,\n",
    "    num_layers, dropout)\n",
    "decoder = TransformerDecoder(\n",
    "    len(tgt_vocab), key_size, query_size, value_size, num_hiddens,\n",
    "    norm_shape, ffn_num_input, ffn_num_hiddens, num_heads,\n",
    "    num_layers, dropout)\n",
    "net = d2l.EncoderDecoder(encoder, decoder)\n",
    "d2l.train_seq2seq(net, train_iter, lr, num_epochs, tgt_vocab, device)"
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  },
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   "id": "07b91e72",
   "metadata": {
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   },
   "source": [
    "将一些英语句子翻译成法语"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "24013ec6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:03:11.622906Z",
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     "shell.execute_reply": "2022-07-31T03:03:11.728795Z"
    },
    "origin_pos": 57,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "go . => va !,  bleu 1.000\n",
      "i lost . => je suis avons été battues .,  bleu 0.000\n",
      "he's calm . => il est malade .,  bleu 0.658\n",
      "i'm home . => je suis chez moi .,  bleu 1.000\n"
     ]
    }
   ],
   "source": [
    "engs = ['go .', \"i lost .\", 'he\\'s calm .', 'i\\'m home .']\n",
    "fras = ['va !', 'j\\'ai perdu .', 'il est calme .', 'je suis chez moi .']\n",
    "for eng, fra in zip(engs, fras):\n",
    "    translation, dec_attention_weight_seq = d2l.predict_seq2seq(\n",
    "        net, eng, src_vocab, tgt_vocab, num_steps, device, True)\n",
    "    print(f'{eng} => {translation}, ',\n",
    "          f'bleu {d2l.bleu(translation, fra, k=2):.3f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab7e6d34",
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   "source": [
    "可视化transformer的注意力权重"
   ]
  },
  {
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   "execution_count": 16,
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    "tab": [
     "pytorch"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([2, 4, 10, 10])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "enc_attention_weights = torch.cat(net.encoder.attention_weights, 0).reshape((num_layers, num_heads,\n",
    "    -1, num_steps))\n",
    "enc_attention_weights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0cae647a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:03:11.741370Z",
     "iopub.status.busy": "2022-07-31T03:03:11.740883Z",
     "iopub.status.idle": "2022-07-31T03:03:12.519385Z",
     "shell.execute_reply": "2022-07-31T03:03:12.518542Z"
    },
    "origin_pos": 63,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
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   ],
   "source": [
    "d2l.show_heatmaps(\n",
    "    enc_attention_weights.cpu(), xlabel='Key positions',\n",
    "    ylabel='Query positions', titles=['Head %d' % i for i in range(1, 5)],\n",
    "    figsize=(7, 3.5))"
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  {
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   "id": "2a3bd761",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "为了可视化解码器的自注意力权重和“编码器－解码器”的注意力权重，我们需要完成更多的数据操作工作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a18c2770",
   "metadata": {
    "execution": {
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     "iopub.status.busy": "2022-07-31T03:03:12.522171Z",
     "iopub.status.idle": "2022-07-31T03:03:12.535162Z",
     "shell.execute_reply": "2022-07-31T03:03:12.534265Z"
    },
    "origin_pos": 66,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([2, 4, 6, 10]), torch.Size([2, 4, 6, 10]))"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dec_attention_weights_2d = [head[0].tolist()\n",
    "                            for step in dec_attention_weight_seq\n",
    "                            for attn in step for blk in attn for head in blk]\n",
    "dec_attention_weights_filled = torch.tensor(\n",
    "    pd.DataFrame(dec_attention_weights_2d).fillna(0.0).values)\n",
    "dec_attention_weights = dec_attention_weights_filled.reshape((-1, 2, num_layers, num_heads, num_steps))\n",
    "dec_self_attention_weights, dec_inter_attention_weights = \\\n",
    "    dec_attention_weights.permute(1, 2, 3, 0, 4)\n",
    "dec_self_attention_weights.shape, dec_inter_attention_weights.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "065c21cf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:03:12.539466Z",
     "iopub.status.busy": "2022-07-31T03:03:12.538760Z",
     "iopub.status.idle": "2022-07-31T03:03:13.233536Z",
     "shell.execute_reply": "2022-07-31T03:03:13.232656Z"
    },
    "origin_pos": 69,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
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      "text/plain": [
       "<Figure size 504x252 with 9 Axes>"
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   ],
   "source": [
    "d2l.show_heatmaps(\n",
    "    dec_self_attention_weights[:, :, :, :len(translation.split()) + 1],\n",
    "    xlabel='Key positions', ylabel='Query positions',\n",
    "    titles=['Head %d' % i for i in range(1, 5)], figsize=(7, 3.5))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "150203f1",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "输出序列的查询不会与输入序列中填充位置的词元进行注意力计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c4229176",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-31T03:03:13.236550Z",
     "iopub.status.busy": "2022-07-31T03:03:13.236271Z",
     "iopub.status.idle": "2022-07-31T03:03:13.917649Z",
     "shell.execute_reply": "2022-07-31T03:03:13.916772Z"
    },
    "origin_pos": 71,
    "tab": [
     "pytorch"
    ]
   },
   "outputs": [
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    "d2l.show_heatmaps(\n",
    "    dec_inter_attention_weights, xlabel='Key positions',\n",
    "    ylabel='Query positions', titles=['Head %d' % i for i in range(1, 5)],\n",
    "    figsize=(7, 3.5))"
   ]
  }
 ],
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